{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\Han\Desktop\document\불평등 연구\주제14_내로남불_교육\social_sciences\data\analysis_3.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}10 Nov 2024, 13:19:31

{com}. meoprobit edu_fair class education age gender marital religion urban i.year if ideology==2||country:, nolog
{res}
{txt}Mixed-effects oprobit regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    12,797
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        34

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        16
{col 63}{txt}avg{col 67}={res}{col 69}     376.4
{col 63}{txt}max{col 67}={res}{col 69}     1,010

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}8{txt}){col 67}={res}{col 70}   234.56
{txt}Log likelihood = {res}-16799.099{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    edu_fair{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}class {c |}{col 14}{res}{space 2} .0532393{col 26}{space 2} .0064486{col 37}{space 1}    8.26{col 46}{space 3}0.000{col 54}{space 4} .0406003{col 67}{space 3} .0658783
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0499774{col 26}{space 2} .0080008{col 37}{space 1}   -6.25{col 46}{space 3}0.000{col 54}{space 4}-.0656588{col 67}{space 3}-.0342961
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0138522{col 26}{space 2} .0066203{col 37}{space 1}   -2.09{col 46}{space 3}0.036{col 54}{space 4}-.0268278{col 67}{space 3}-.0008767
{txt}{space 6}gender {c |}{col 14}{res}{space 2} -.145678{col 26}{space 2} .0197588{col 37}{space 1}   -7.37{col 46}{space 3}0.000{col 54}{space 4}-.1844046{col 67}{space 3}-.1069515
{txt}{space 5}marital {c |}{col 14}{res}{space 2} .0243848{col 26}{space 2} .0203581{col 37}{space 1}    1.20{col 46}{space 3}0.231{col 54}{space 4}-.0155163{col 67}{space 3}  .064286
{txt}{space 4}religion {c |}{col 14}{res}{space 2} .1705682{col 26}{space 2} .0226862{col 37}{space 1}    7.52{col 46}{space 3}0.000{col 54}{space 4}  .126104{col 67}{space 3} .2150323
{txt}{space 7}urban {c |}{col 14}{res}{space 2} .0487858{col 26}{space 2} .0085084{col 37}{space 1}    5.73{col 46}{space 3}0.000{col 54}{space 4} .0321097{col 67}{space 3}  .065462
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2019  {c |}{col 14}{res}{space 2}  .010797{col 26}{space 2}  .030101{col 37}{space 1}    0.36{col 46}{space 3}0.720{col 54}{space 4}-.0481999{col 67}{space 3} .0697938
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-.0676245{col 26}{space 2} .0932442{col 37}{space 1}   -0.73{col 46}{space 3}0.468{col 54}{space 4}-.2503798{col 67}{space 3} .1151308
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2} .7729004{col 26}{space 2} .0934347{col 37}{space 1}    8.27{col 46}{space 3}0.000{col 54}{space 4} .5897717{col 67}{space 3}  .956029
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2} 1.342321{col 26}{space 2} .0937394{col 37}{space 1}   14.32{col 46}{space 3}0.000{col 54}{space 4} 1.158595{col 67}{space 3} 1.526046
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2}  2.03659{col 26}{space 2} .0948554{col 37}{space 1}   21.47{col 46}{space 3}0.000{col 54}{space 4} 1.850676{col 67}{space 3} 2.222503
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .1798874{col 26}{space 2} .0459363{col 54}{space 4} .1090528{col 67}{space 3} .2967321
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. oprobit model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 1249.97{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. 
. 
. 
. meoprobit edu_fair class income education age gender marital religion urban i.year if ideology==2||country:, nolog
{res}
{txt}Mixed-effects oprobit regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    11,483
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        34

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        12
{col 63}{txt}avg{col 67}={res}{col 69}     337.7
{col 63}{txt}max{col 67}={res}{col 69}       945

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}9{txt}){col 67}={res}{col 70}   230.94
{txt}Log likelihood = {res}-14957.244{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    edu_fair{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}class {c |}{col 14}{res}{space 2} .0501592{col 26}{space 2} .0069625{col 37}{space 1}    7.20{col 46}{space 3}0.000{col 54}{space 4}  .036513{col 67}{space 3} .0638055
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0291516{col 26}{space 2} .0105464{col 37}{space 1}    2.76{col 46}{space 3}0.006{col 54}{space 4} .0084809{col 67}{space 3} .0498223
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0580928{col 26}{space 2} .0087811{col 37}{space 1}   -6.62{col 46}{space 3}0.000{col 54}{space 4}-.0753034{col 67}{space 3}-.0408822
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0121749{col 26}{space 2} .0070069{col 37}{space 1}   -1.74{col 46}{space 3}0.082{col 54}{space 4}-.0259082{col 67}{space 3} .0015583
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.1444432{col 26}{space 2} .0214372{col 37}{space 1}   -6.74{col 46}{space 3}0.000{col 54}{space 4}-.1864594{col 67}{space 3} -.102427
{txt}{space 5}marital {c |}{col 14}{res}{space 2} .0103753{col 26}{space 2} .0216444{col 37}{space 1}    0.48{col 46}{space 3}0.632{col 54}{space 4}-.0320469{col 67}{space 3} .0527975
{txt}{space 4}religion {c |}{col 14}{res}{space 2} .1793576{col 26}{space 2} .0239501{col 37}{space 1}    7.49{col 46}{space 3}0.000{col 54}{space 4} .1324163{col 67}{space 3} .2262988
{txt}{space 7}urban {c |}{col 14}{res}{space 2}  .048012{col 26}{space 2} .0090344{col 37}{space 1}    5.31{col 46}{space 3}0.000{col 54}{space 4} .0303049{col 67}{space 3} .0657192
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2019  {c |}{col 14}{res}{space 2} .0305173{col 26}{space 2} .0313855{col 37}{space 1}    0.97{col 46}{space 3}0.331{col 54}{space 4}-.0309971{col 67}{space 3} .0920318
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-.0218348{col 26}{space 2} .0950879{col 37}{space 1}   -0.23{col 46}{space 3}0.818{col 54}{space 4}-.2082037{col 67}{space 3} .1645341
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2} .8237371{col 26}{space 2} .0953297{col 37}{space 1}    8.64{col 46}{space 3}0.000{col 54}{space 4} .6368943{col 67}{space 3}  1.01058
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2} 1.389508{col 26}{space 2} .0956749{col 37}{space 1}   14.52{col 46}{space 3}0.000{col 54}{space 4} 1.201989{col 67}{space 3} 1.577028
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} 2.082226{col 26}{space 2} .0969175{col 37}{space 1}   21.48{col 46}{space 3}0.000{col 54}{space 4} 1.892272{col 67}{space 3} 2.272181
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .1701605{col 26}{space 2} .0435508{col 54}{space 4} .1030392{col 67}{space 3} .2810056
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. oprobit model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 1061.99{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. 
. 
. 
. meoprobit edu_fair class income employer high_skilled education age gender marital religion urban i.year if ideology==2||country:, nolog
{res}
{txt}Mixed-effects oprobit regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    11,483
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        34

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        12
{col 63}{txt}avg{col 67}={res}{col 69}     337.7
{col 63}{txt}max{col 67}={res}{col 69}       945

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}11{txt}){col 67}={res}{col 70}   233.62
{txt}Log likelihood = {res}-14955.895{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    edu_fair{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}class {c |}{col 14}{res}{space 2} .0490861{col 26}{space 2} .0069966{col 37}{space 1}    7.02{col 46}{space 3}0.000{col 54}{space 4} .0353729{col 67}{space 3} .0627993
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0262589{col 26}{space 2} .0107787{col 37}{space 1}    2.44{col 46}{space 3}0.015{col 54}{space 4}  .005133{col 67}{space 3} .0473847
{txt}{space 4}employer {c |}{col 14}{res}{space 2} .0733103{col 26}{space 2} .0661101{col 37}{space 1}    1.11{col 46}{space 3}0.267{col 54}{space 4}-.0562631{col 67}{space 3} .2028837
{txt}high_skilled {c |}{col 14}{res}{space 2} .0296932{col 26}{space 2} .0252325{col 37}{space 1}    1.18{col 46}{space 3}0.239{col 54}{space 4}-.0197615{col 67}{space 3}  .079148
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0624846{col 26}{space 2} .0095337{col 37}{space 1}   -6.55{col 46}{space 3}0.000{col 54}{space 4}-.0811704{col 67}{space 3}-.0437988
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0131649{col 26}{space 2} .0070433{col 37}{space 1}   -1.87{col 46}{space 3}0.062{col 54}{space 4}-.0269696{col 67}{space 3} .0006398
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.1455834{col 26}{space 2} .0215175{col 37}{space 1}   -6.77{col 46}{space 3}0.000{col 54}{space 4}-.1877569{col 67}{space 3}-.1034099
{txt}{space 5}marital {c |}{col 14}{res}{space 2} .0097315{col 26}{space 2} .0216492{col 37}{space 1}    0.45{col 46}{space 3}0.653{col 54}{space 4}-.0327002{col 67}{space 3} .0521631
{txt}{space 4}religion {c |}{col 14}{res}{space 2} .1804101{col 26}{space 2} .0239604{col 37}{space 1}    7.53{col 46}{space 3}0.000{col 54}{space 4} .1334487{col 67}{space 3} .2273716
{txt}{space 7}urban {c |}{col 14}{res}{space 2} .0478219{col 26}{space 2}  .009041{col 37}{space 1}    5.29{col 46}{space 3}0.000{col 54}{space 4} .0301018{col 67}{space 3} .0655419
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2019  {c |}{col 14}{res}{space 2} .0339726{col 26}{space 2} .0314566{col 37}{space 1}    1.08{col 46}{space 3}0.280{col 54}{space 4}-.0276812{col 67}{space 3} .0956264
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-.0390421{col 26}{space 2} .0963224{col 37}{space 1}   -0.41{col 46}{space 3}0.685{col 54}{space 4}-.2278305{col 67}{space 3} .1497464
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2} .8066592{col 26}{space 2} .0965502{col 37}{space 1}    8.35{col 46}{space 3}0.000{col 54}{space 4} .6174242{col 67}{space 3} .9958941
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2} 1.372634{col 26}{space 2} .0968785{col 37}{space 1}   14.17{col 46}{space 3}0.000{col 54}{space 4} 1.182756{col 67}{space 3} 1.562513
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} 2.065551{col 26}{space 2} .0981012{col 37}{space 1}   21.06{col 46}{space 3}0.000{col 54}{space 4} 1.873276{col 67}{space 3} 2.257826
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .1704329{col 26}{space 2} .0436244{col 54}{space 4} .1031996{col 67}{space 3} .2814681
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. oprobit model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 1056.52{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. 
. estimates store edu_5

. meoprobit edu_fair class education age gender marital religion urban i.year if ideology==1||country:, nolog
{res}
{txt}Mixed-effects oprobit regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    11,398
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        34

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         8
{col 63}{txt}avg{col 67}={res}{col 69}     335.2
{col 63}{txt}max{col 67}={res}{col 69}     1,204

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}8{txt}){col 67}={res}{col 70}   263.32
{txt}Log likelihood = {res}-16717.961{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    edu_fair{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}class {c |}{col 14}{res}{space 2} .0629312{col 26}{space 2}  .006945{col 37}{space 1}    9.06{col 46}{space 3}0.000{col 54}{space 4} .0493193{col 67}{space 3} .0765431
{txt}{space 3}education {c |}{col 14}{res}{space 2} .0461434{col 26}{space 2} .0084331{col 37}{space 1}    5.47{col 46}{space 3}0.000{col 54}{space 4} .0296148{col 67}{space 3} .0626719
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0077897{col 26}{space 2} .0067913{col 37}{space 1}   -1.15{col 46}{space 3}0.251{col 54}{space 4}-.0211004{col 67}{space 3} .0055211
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.1248034{col 26}{space 2} .0201564{col 37}{space 1}   -6.19{col 46}{space 3}0.000{col 54}{space 4}-.1643092{col 67}{space 3}-.0852977
{txt}{space 5}marital {c |}{col 14}{res}{space 2}-.0011343{col 26}{space 2} .0215314{col 37}{space 1}   -0.05{col 46}{space 3}0.958{col 54}{space 4}-.0433352{col 67}{space 3} .0410665
{txt}{space 4}religion {c |}{col 14}{res}{space 2}-.0065959{col 26}{space 2} .0261552{col 37}{space 1}   -0.25{col 46}{space 3}0.801{col 54}{space 4}-.0578592{col 67}{space 3} .0446674
{txt}{space 7}urban {c |}{col 14}{res}{space 2} .0441035{col 26}{space 2} .0088247{col 37}{space 1}    5.00{col 46}{space 3}0.000{col 54}{space 4} .0268074{col 67}{space 3} .0613997
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2019  {c |}{col 14}{res}{space 2}-.1031112{col 26}{space 2} .0285976{col 37}{space 1}   -3.61{col 46}{space 3}0.000{col 54}{space 4}-.1591614{col 67}{space 3} -.047061
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-.2100996{col 26}{space 2} .1000916{col 37}{space 1}   -2.10{col 46}{space 3}0.036{col 54}{space 4}-.4062756{col 67}{space 3}-.0139236
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2} .5731446{col 26}{space 2} .1002551{col 37}{space 1}    5.72{col 46}{space 3}0.000{col 54}{space 4} .3766482{col 67}{space 3}  .769641
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2} 1.257659{col 26}{space 2} .1005506{col 37}{space 1}   12.51{col 46}{space 3}0.000{col 54}{space 4} 1.060584{col 67}{space 3} 1.454735
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} 2.095138{col 26}{space 2} .1014029{col 37}{space 1}   20.66{col 46}{space 3}0.000{col 54}{space 4} 1.896392{col 67}{space 3} 2.293884
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .2189861{col 26}{space 2} .0552707{col 54}{space 4} .1335299{col 67}{space 3} .3591324
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. oprobit model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 1284.00{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. 
. 
. 
. meoprobit edu_fair class income education age gender marital religion urban i.year if ideology==1||country:, nolog
{res}
{txt}Mixed-effects oprobit regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    10,153
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        34

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         7
{col 63}{txt}avg{col 67}={res}{col 69}     298.6
{col 63}{txt}max{col 67}={res}{col 69}     1,063

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}9{txt}){col 67}={res}{col 70}   273.52
{txt}Log likelihood = {res}-14881.003{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    edu_fair{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}class {c |}{col 14}{res}{space 2} .0573868{col 26}{space 2} .0074984{col 37}{space 1}    7.65{col 46}{space 3}0.000{col 54}{space 4} .0426902{col 67}{space 3} .0720834
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0472339{col 26}{space 2} .0105413{col 37}{space 1}    4.48{col 46}{space 3}0.000{col 54}{space 4} .0265734{col 67}{space 3} .0678944
{txt}{space 3}education {c |}{col 14}{res}{space 2} .0372073{col 26}{space 2} .0092085{col 37}{space 1}    4.04{col 46}{space 3}0.000{col 54}{space 4}  .019159{col 67}{space 3} .0552556
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0086371{col 26}{space 2} .0072021{col 37}{space 1}   -1.20{col 46}{space 3}0.230{col 54}{space 4} -.022753{col 67}{space 3} .0054789
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.1155254{col 26}{space 2} .0222141{col 37}{space 1}   -5.20{col 46}{space 3}0.000{col 54}{space 4}-.1590642{col 67}{space 3}-.0719865
{txt}{space 5}marital {c |}{col 14}{res}{space 2}-.0213708{col 26}{space 2} .0230022{col 37}{space 1}   -0.93{col 46}{space 3}0.353{col 54}{space 4}-.0664543{col 67}{space 3} .0237127
{txt}{space 4}religion {c |}{col 14}{res}{space 2} -.004656{col 26}{space 2} .0275842{col 37}{space 1}   -0.17{col 46}{space 3}0.866{col 54}{space 4}-.0587201{col 67}{space 3} .0494081
{txt}{space 7}urban {c |}{col 14}{res}{space 2} .0444567{col 26}{space 2}  .009353{col 37}{space 1}    4.75{col 46}{space 3}0.000{col 54}{space 4} .0261251{col 67}{space 3} .0627883
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2019  {c |}{col 14}{res}{space 2}-.0892611{col 26}{space 2} .0300847{col 37}{space 1}   -2.97{col 46}{space 3}0.003{col 54}{space 4}-.1482261{col 67}{space 3}-.0302962
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2} -.146254{col 26}{space 2} .1029086{col 37}{space 1}   -1.42{col 46}{space 3}0.155{col 54}{space 4}-.3479511{col 67}{space 3} .0554431
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2} .6363939{col 26}{space 2} .1031301{col 37}{space 1}    6.17{col 46}{space 3}0.000{col 54}{space 4} .4342626{col 67}{space 3} .8385252
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2} 1.324618{col 26}{space 2} .1034733{col 37}{space 1}   12.80{col 46}{space 3}0.000{col 54}{space 4} 1.121814{col 67}{space 3} 1.527422
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} 2.159614{col 26}{space 2} .1044006{col 37}{space 1}   20.69{col 46}{space 3}0.000{col 54}{space 4} 1.954992{col 67}{space 3} 2.364235
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .2164518{col 26}{space 2} .0548951{col 54}{space 4} .1316695{col 67}{space 3} .3558257
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. oprobit model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 1151.25{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. 
. 
. 
. meoprobit edu_fair class income employer high_skilled education age gender marital religion urban i.year if ideology==1||country:, nolog
{res}
{txt}Mixed-effects oprobit regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    10,153
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        34

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         7
{col 63}{txt}avg{col 67}={res}{col 69}     298.6
{col 63}{txt}max{col 67}={res}{col 69}     1,063

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}11{txt}){col 67}={res}{col 70}   277.26
{txt}Log likelihood = {res}-14879.125{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    edu_fair{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}class {c |}{col 14}{res}{space 2} .0563778{col 26}{space 2} .0075537{col 37}{space 1}    7.46{col 46}{space 3}0.000{col 54}{space 4} .0415729{col 67}{space 3} .0711827
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0453686{col 26}{space 2} .0108467{col 37}{space 1}    4.18{col 46}{space 3}0.000{col 54}{space 4} .0241095{col 67}{space 3} .0666276
{txt}{space 4}employer {c |}{col 14}{res}{space 2}  .081399{col 26}{space 2} .0434819{col 37}{space 1}    1.87{col 46}{space 3}0.061{col 54}{space 4} -.003824{col 67}{space 3} .1666221
{txt}high_skilled {c |}{col 14}{res}{space 2} .0111899{col 26}{space 2} .0255209{col 37}{space 1}    0.44{col 46}{space 3}0.661{col 54}{space 4}-.0388301{col 67}{space 3} .0612099
{txt}{space 3}education {c |}{col 14}{res}{space 2} .0356169{col 26}{space 2} .0097477{col 37}{space 1}    3.65{col 46}{space 3}0.000{col 54}{space 4} .0165117{col 67}{space 3}  .054722
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0098002{col 26}{space 2} .0072448{col 37}{space 1}   -1.35{col 46}{space 3}0.176{col 54}{space 4}-.0239998{col 67}{space 3} .0043994
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.1126795{col 26}{space 2} .0223124{col 37}{space 1}   -5.05{col 46}{space 3}0.000{col 54}{space 4} -.156411{col 67}{space 3} -.068948
{txt}{space 5}marital {c |}{col 14}{res}{space 2}-.0227148{col 26}{space 2} .0230133{col 37}{space 1}   -0.99{col 46}{space 3}0.324{col 54}{space 4}-.0678199{col 67}{space 3} .0223904
{txt}{space 4}religion {c |}{col 14}{res}{space 2} -.005062{col 26}{space 2}  .027586{col 37}{space 1}   -0.18{col 46}{space 3}0.854{col 54}{space 4}-.0591295{col 67}{space 3} .0490055
{txt}{space 7}urban {c |}{col 14}{res}{space 2} .0449806{col 26}{space 2} .0093695{col 37}{space 1}    4.80{col 46}{space 3}0.000{col 54}{space 4} .0266167{col 67}{space 3} .0633445
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2019  {c |}{col 14}{res}{space 2}-.0852006{col 26}{space 2} .0301569{col 37}{space 1}   -2.83{col 46}{space 3}0.005{col 54}{space 4} -.144307{col 67}{space 3}-.0260943
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2} -.154554{col 26}{space 2} .1040248{col 37}{space 1}   -1.49{col 46}{space 3}0.137{col 54}{space 4} -.358439{col 67}{space 3} .0493309
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2} .6282093{col 26}{space 2} .1042421{col 37}{space 1}    6.03{col 46}{space 3}0.000{col 54}{space 4} .4238984{col 67}{space 3} .8325201
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2} 1.316641{col 26}{space 2} .1045709{col 37}{space 1}   12.59{col 46}{space 3}0.000{col 54}{space 4} 1.111686{col 67}{space 3} 1.521596
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2}  2.15189{col 26}{space 2} .1054817{col 37}{space 1}   20.40{col 46}{space 3}0.000{col 54}{space 4}  1.94515{col 67}{space 3}  2.35863
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .2160075{col 26}{space 2} .0547783{col 54}{space 4} .1314041{col 67}{space 3} .3550821
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. oprobit model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 1146.39{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. 
. estimates store edu_6

. 
. 
. 
. 
. 
. coefplot (edu_5) (edu_6) , eform drop(_cons) omitted xline(1, lcolor(black) lwidth(thin)) lpattern(dash) graphregion(fcolor(white)) mlabel format(%9.2f) mlabposition(8) mlabsize(tiny) level(95) keep (class) 
{res}
{com}. coefplot (edu_1) (edu_3) (edu_5)||(edu_2) (edu_4) (edu_6), eform drop(_cons) omitted yline(1, lcolor(black) lwidth(thin)) lpattern(dash) graphregion(fcolor(white)) mlabel format(%9.2f) mlabposition(8) mlabsize(tiny) level(95) keep (class) vertical
{res}
{com}. 
. 
. 
. 
. 
. 
. 
. 
. 
. meoprobit edu_fair class income employer high_skilled education age gender marital religion urban i.year if ideology==2||country:, nolog
{res}
{txt}Mixed-effects oprobit regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    11,483
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        34

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        12
{col 63}{txt}avg{col 67}={res}{col 69}     337.7
{col 63}{txt}max{col 67}={res}{col 69}       945

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}11{txt}){col 67}={res}{col 70}   233.62
{txt}Log likelihood = {res}-14955.895{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    edu_fair{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}class {c |}{col 14}{res}{space 2} .0490861{col 26}{space 2} .0069966{col 37}{space 1}    7.02{col 46}{space 3}0.000{col 54}{space 4} .0353729{col 67}{space 3} .0627993
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0262589{col 26}{space 2} .0107787{col 37}{space 1}    2.44{col 46}{space 3}0.015{col 54}{space 4}  .005133{col 67}{space 3} .0473847
{txt}{space 4}employer {c |}{col 14}{res}{space 2} .0733103{col 26}{space 2} .0661101{col 37}{space 1}    1.11{col 46}{space 3}0.267{col 54}{space 4}-.0562631{col 67}{space 3} .2028837
{txt}high_skilled {c |}{col 14}{res}{space 2} .0296932{col 26}{space 2} .0252325{col 37}{space 1}    1.18{col 46}{space 3}0.239{col 54}{space 4}-.0197615{col 67}{space 3}  .079148
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0624846{col 26}{space 2} .0095337{col 37}{space 1}   -6.55{col 46}{space 3}0.000{col 54}{space 4}-.0811704{col 67}{space 3}-.0437988
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0131649{col 26}{space 2} .0070433{col 37}{space 1}   -1.87{col 46}{space 3}0.062{col 54}{space 4}-.0269696{col 67}{space 3} .0006398
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.1455834{col 26}{space 2} .0215175{col 37}{space 1}   -6.77{col 46}{space 3}0.000{col 54}{space 4}-.1877569{col 67}{space 3}-.1034099
{txt}{space 5}marital {c |}{col 14}{res}{space 2} .0097315{col 26}{space 2} .0216492{col 37}{space 1}    0.45{col 46}{space 3}0.653{col 54}{space 4}-.0327002{col 67}{space 3} .0521631
{txt}{space 4}religion {c |}{col 14}{res}{space 2} .1804101{col 26}{space 2} .0239604{col 37}{space 1}    7.53{col 46}{space 3}0.000{col 54}{space 4} .1334487{col 67}{space 3} .2273716
{txt}{space 7}urban {c |}{col 14}{res}{space 2} .0478219{col 26}{space 2}  .009041{col 37}{space 1}    5.29{col 46}{space 3}0.000{col 54}{space 4} .0301018{col 67}{space 3} .0655419
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2019  {c |}{col 14}{res}{space 2} .0339726{col 26}{space 2} .0314566{col 37}{space 1}    1.08{col 46}{space 3}0.280{col 54}{space 4}-.0276812{col 67}{space 3} .0956264
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-.0390421{col 26}{space 2} .0963224{col 37}{space 1}   -0.41{col 46}{space 3}0.685{col 54}{space 4}-.2278305{col 67}{space 3} .1497464
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2} .8066592{col 26}{space 2} .0965502{col 37}{space 1}    8.35{col 46}{space 3}0.000{col 54}{space 4} .6174242{col 67}{space 3} .9958941
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2} 1.372634{col 26}{space 2} .0968785{col 37}{space 1}   14.17{col 46}{space 3}0.000{col 54}{space 4} 1.182756{col 67}{space 3} 1.562513
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} 2.065551{col 26}{space 2} .0981012{col 37}{space 1}   21.06{col 46}{space 3}0.000{col 54}{space 4} 1.873276{col 67}{space 3} 2.257826
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .1704329{col 26}{space 2} .0436244{col 54}{space 4} .1031996{col 67}{space 3} .2814681
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. oprobit model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 1056.52{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. 
. 
. 
. 
. 
. margins, at(class =(1(1)10)) atmeans noatlegend 
{res}{err}{hline 2}Break{hline 2}
{txt}{search r(1), local:r(1);}

{com}. end
{err}command {bf}end{sf} is unrecognized
{txt}{search r(199), local:r(199);}

{com}. save "C:\Users\Han\Desktop\document\불평등 연구\주제14_내로남불_교육\social_sciences\data\df.dta"
{txt}file C:\Users\Han\Desktop\document\불평등 연구\주제14_내로남불_교육\social_sciences\data\df.dta saved

{com}. exit
